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March 2020 Estimating and forecasting the smoking-attributable mortality fraction for both genders jointly in over 60 countries
Yicheng Li, Adrian E. Raftery
Ann. Appl. Stat. 14(1): 381-408 (March 2020). DOI: 10.1214/19-AOAS1306


Smoking is one of the leading preventable threats to human health and a major risk factor for lung cancer, upper aerodigestive cancer and chronic obstructive pulmonary disease. Estimating and forecasting the smoking attributable fraction (SAF) of mortality can yield insights into smoking epidemics and also provide a basis for more accurate mortality and life expectancy projection. Peto et al. (Lancet 339 (1992) 1268–1278) proposed a method to estimate the SAF using the lung cancer mortality rate as an indicator of exposure to smoking in the population of interest. Here, we use the same method to estimate the all-age SAF (ASAF) for both genders for over 60 countries. We document a strong and cross-nationally consistent pattern of the evolution of the SAF over time. We use this as the basis for a new Bayesian hierarchical model to project future male and female ASAF from over 60 countries simultaneously. This gives forecasts as well as predictive distributions that can be used to find uncertainty intervals for any quantity of interest. We assess the model using out-of-sample predictive validation and find that it provides good forecasts and well-calibrated forecast intervals, comparing favorably with other methods.


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Yicheng Li. Adrian E. Raftery. "Estimating and forecasting the smoking-attributable mortality fraction for both genders jointly in over 60 countries." Ann. Appl. Stat. 14 (1) 381 - 408, March 2020.


Received: 1 February 2019; Revised: 1 October 2019; Published: March 2020
First available in Project Euclid: 16 April 2020

zbMATH: 07200176
MathSciNet: MR4085098
Digital Object Identifier: 10.1214/19-AOAS1306

Keywords: Bayesian hierarchical model , double logistic curve , Peto–Lopez method , probabilislic projection , Smoking attributable fraction

Rights: Copyright © 2020 Institute of Mathematical Statistics


Vol.14 • No. 1 • March 2020
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